UC Santa Barbara

This study develops an integrative methodological framework that combines forensic hydrology, artificial neural networks (ANNs), and physically-based hydrological modeling to assess the cascading impacts of projected land use/land cover (LULC) changes on future flood dynamics in the upstream watershed of Golestan Dam, Iran. A large-magnitude flood event analogous to the destructive 2019 flood was simulated under evolving LULC scenarios for three future horizons: 2030, 2040, and 2050. The curve number (CN) and lag time (Tlag) were reconstructed using linear and nonlinear interpolation and projected with ANN-based regression modeling. Mann-Kendall trend analysis was applied to assess the temporal consistency of ANN-predicted CN and Tlag series and support the selection of the most reliable synthetic datasets. The simulations revealed highly non-linear growth in peak discharges, with the Qarah Shur sub-basin projected to experience a 163.78% increase in flood intensity by 2050, resulting from a substantial increase in the contribution of barren land to flood intensification (from 22.89% to 78.88%) and a threefold increase in the contribution of sparse vegetation (from 9.04% to 31.13%). These two factors, individually and in combination, were identified as the most critical contributors to flood amplification in this sub-basin. The Galikesh sub-basin followed with a 68.99% increase by 2050, being the only sub-basin where the contribution of road expansion (from 8.88% to 27.41%) emerged as the dominant factor driving flood intensification. The Tamar Gorgan and Tangerah sub-basins demonstrated more moderate increases in discharge, 13.89% and 10.44%, respectively, driven by modest land degradation and road building. This work’s findings underscore growing risks to infrastructure, including embankment instability, bridge scour, and urban inundation, and to groundwater recharge, aquatic ecosystems, and water quality through the mobilization of pollutants and sediments under increasing flood risk. The spatial comparison of sub-basin-level responses captures the heterogeneity of flood vulnerability and the urgency of instituting adaptive land use policies grounded in hydrological sensitivity, riparian corridor protection, and nature-based flood mitigation.

Background Dengue cases are increasing in non-endemic regions due to environmental change and increasing travel and trade. For these non-endemic regions, estimating dengue risk is challenging as transmission is driven by both local environmental conditions and the introduction of viremic travelers. In this study, we aimed to estimate current and future dengue risk in California, USA—a region that has recently experienced its first cases of locally-acquired dengue. Methods We modeled dengue risk as the product of three key components needed for local transmission—vector presence, temperature-suitability for pathogen transmission, and viral introductions via travel-associated cases—estimated using vector and case surveillance, sociodemographic, and environmental data. We estimated risk for locations and months where local transmission was reported in 2023–2024 to define a ‘threshold’ level of risk. We then projected monthly, census tract-level risk under both current conditions and future scenarios of climate warming and urban expansion. Findings Approximately 18.2 million (95% CI: 17.9–18.3) California residents—primarily in the Central Valley and the Los Angeles and San Diego metropolitan areas—currently live in areas where peak monthly dengue risk exceeds levels estimated during observed local transmission. Under moderate scenarios of climate warming and urban expansion, an additional 4.1 million (95% CI: 3.7–4.6) residents may be at risk by mid-century. Outside the summer months and beyond the Central Valley and southern California, current and future risk remains low due to one or more major bottlenecks to transmission. Interpretation Our study identifies the specific regions and months conducive to dengue transmission in the non-endemic setting of California. At present, this covers a substantial portion of the state and is projected to expand under ongoing climate warming and urbanization. Our results underscore the need for sustained vector control, and timely detection of travel-associated cases. Funding National Science Foundation, National Institute of Food and Agriculture.

The problem that incomplete election returns pose for the calculation of the effective number of parties and related statistics such as the Herfindahl-Hirschman fractionalization index is known. Over the past several decades, different measurement approaches have been proposed, all of which attempt to overcome these challenges. The approach that is chosen may have consequences for theories about party system size. However, this issue has not been rigorously empirically explored to date. Accordingly, we investigate the practical consequences of the strategy for measuring party system size using district-level election data for Ireland over a 30-year period. Our primary contribution is to show that the choice of measure of party system size matters for conclusions about its relationship to both social structure and the electoral system at the level of the electoral district. While none of the three commonly employed inexact measurement approximations (the block, omission, and method of bounds) dominates the other two as a proxy for the exact measure across all tests and situations, our results show that the method of bounds performs the best most of the time. Exceptions include when very large shares of votes or seats go to “minor” parties and independents, or when there is only one minor party or independent; researchers must exercise caution in such instances. Yet even the method of bounds leads to different conclusions in our substantive application from that obtained with complete data. An additional contribution of the paper is to show that both district magnitude and social diversity shape party system size at the district level in Ireland, although arguably not always as hypothesized.

Abstract This study investigates whether individual differences (IDs) in speakers’ perceptions of psycholinguistic dimensions (familiarity, knowledge, transparency, ambiguity, valence, arousal) predict idiom processing advantage over matched novel control phrases in L1 and L2 speakers in a phrase judgment task. Bayesian multilevel models showed that item-level average norms from L1 speakers for familiarity, valence, and arousal facilitated idiom judgment in both groups, whereas transparency and ambiguity had inhibitory effects. Beyond item means, deviations in speaker-specific perceptions from the item-level average norm predicted idiom judgment advantage. In both groups, individuals who rated an idiom as more positive and more ambiguous than its item-level average norm showed larger idiom judgment advantages. In L1–L2 comparisons, individual-level deviation effects of valence and transparency were stronger in L2 than in L1 speakers, whereas ambiguity deviation effects were stronger in L1 speakers. Overall, modeling ID effects beyond group norms reveals finer-grained L1–L2 differences in language processing.

Rhodium supported on titania (Rh/TiO 2 ) is an active catalyst for the reverse water gas shift reaction, yet the nature of the active sites for this reaction and others remain under debate due to the dynamic nature of the Rh coordination. Rhodium supported on titania (Rh/TiO 2 ) is an active catalyst for the reverse water gas shift reaction, yet the nature of the active sites for this reaction and others remain under debate. Single atom Rh sites have frequently been proposed as key sites, making it essential to monitor size changes of Rh species under in situ conditions to establish the structure–function relationship. However, surface-sensitive in situ measurements of nanosized particles remain experimentally challenging and have focused on metal oxide single crystal model systems. Here, we apply ambient pressure X-ray photoelectron spectroscopy (APXPS) to Rh/TiO 2 powdered catalysts under oxidizing and reducing environments. We find size-dependent binding energy shifts in both oxidized and reduced Rh species. By deconvoluting this size effect from oxidation state core level shifts, APXPS can provide qualitative evidence of Rh cluster size changes. Potential electronic effects responsible for these shifts are explored with density functional theory calculations. Ex situ transmission electron microscopy gives insight into particle size while Raman spectroscopy identifies Rh oxide phases. Correlative X-ray absorption spectroscopy and pair distribution function (PDF) measurements confirm the structural changes in the APXPS results. These findings offer direct insight into the dynamic behavior of Rh catalyst sintering and fragmentation based on core-level shifts.

Visual systems evolved to extract behaviorally relevant information while animals move through and interact with their world. Such ecological vision differs fundamentally from standard laboratory paradigms in many key aspects, making this a much harder problem for the brain to solve, and for the neuroscientist to study. However, emerging technologies and experimental approaches have enabled investigation of visual computations under these ecological conditions. These approaches are particularly powerful in the mouse, combining well-developed genetic tools, high-throughput recordings, and quantifiable ethological tasks. Here we review computations that are engaged in ecological contexts, including active sensing, motion processing, scene analysis, distance estimation, and spatial perception. We delineate experimental approaches that engage these computations and synthesize current understanding of their neural implementations based on mouse research. These studies reveal how ecological vision engages distinct processing strategies and novel neural circuitry, while highlighting the vast territory that remains unexplored in understanding real-world visual computation.

Broadening participation and promoting equity in engineering education remain persistent challenges despite decades of national investment. While traditional diversity efforts have emphasized access and recruitment, emerging evidence highlights the importance of understanding how psychological factors shape engagement and learning preferences. This study examines how empathy, engagement, and self-efficacy predict undergraduate engineering and STEM majors’ preferences for curriculum representations across core engineering and physics topics, including static and projectile motion, magnetic forces, and mathematical content such as the Pythagorean theorem. Grounded in a human-centered approach to engineering education, the research investigates how emotional and cognitive factors influence learners’ interactions with textual, symbolic, graphical, and pseudo-realistic visual materials. Data were collected from 964 undergraduate students at four U.S. research universities. Participants completed validated measures of affective and cognitive empathy, behavioral, cognitive, and emotional engagement, and self-efficacy, along with a structured survey assessing preferences for 24 curriculum formats. Partial least squares structural equation modeling was used to analyze relationships among six core psychological constructs and instructional representation preferences. Results indicated that cognitive empathy was associated with preferences for structured, abstract representations such as equations and diagrams, whereas affective empathy aligned with emotionally evocative or pseudo-realistic formats. Self-efficacy functioned as a moderator, with higher confidence linked to minimalist or exploratory visuals and lower confidence associated with scaffolded, visually detailed designs. Cognitive and emotional engagement predicted preferences for expressive or immersive visuals, while behavioral engagement was negatively related to passive formats. These findings demonstrate that empathy, engagement, and self-efficacy interact to shape how students engage with instructional representations. By integrating psychological and pedagogical insights, this work advances engineering education and underscores the value of empathy-informed, multimodal design for fostering inclusion, persistence, and deeper learning.